2017
DOI: 10.1007/s11071-017-3452-y
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Analytical parametric study of bi-linear hysteretic model of dry friction under harmonic, impulse and random excitations

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Cited by 19 publications
(3 citation statements)
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“…Analytical approach is considered for fundamental study of joint capability for suppressing destructive loads. This approach utilizes Jenkins element for friction modeling in joint [3,4]. Energy balance method and time domain response are the means of study.…”
Section: Joints As An Energy Dissipator For Impulsive Loadsmentioning
confidence: 99%
“…Analytical approach is considered for fundamental study of joint capability for suppressing destructive loads. This approach utilizes Jenkins element for friction modeling in joint [3,4]. Energy balance method and time domain response are the means of study.…”
Section: Joints As An Energy Dissipator For Impulsive Loadsmentioning
confidence: 99%
“…For exploring the nonlinear mechanical properties of bolted joints, early on, linear viscous damping models were used to explain hysteresis phenomena in the joint [4].S. Sitzmann et al [5] proposed an algorithm for solving quasi-static, nonlinear elastic contact problems with friction in rough surface environments to simulate the transition from stick-slip to sliding.The Jenkins element, which serves as the basic bolted joint modeling unit [6][7], consists of a spring in series and a Coulomb friction slider.The stick-slip phase of the Jenkins element is described by the yield displacement of the Coulomb slider and the velocity of the structure.The Iwan model is commonly used to characterize the energy dissipation and stiffness properties of connected structures. Based on the contact pressure distribution of parallel Jenkins elements, Iwan models with a uniform distribution function [8] and with a power-law distribution function [9] were proposed, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, many approximate methods have been proposed for predicting the response of the hysteretic system exposed to random excitations. They are the methods of equivalent linearization [7][8][9][10][11], explicit time-domain method [12], probability density evolution method [13][14], Gaussian closure technique [15], exponential-polynomial closure (EPC) method [16][17], dissipation energy balancing method [18], stochastic averaging [19][20][21][22][23][24][25][26], deep neural networks [27], Monte Carlo simulation [28], etc. Among methods mentioned above, the stochastic averaging method has the remarkable advantages in simplifying the equation of motion while retaining the main nonlinear behavior of system [29].…”
Section: Introductionmentioning
confidence: 99%